The influence of credit policy on capital structure via regression analysis

CHEN Yu Ling1,a, BAO Ya Jie2,b*

1 International Education Institute, North China Electric Power University, Beijing, P.R.China

2 School of Economics and Management,North China Electric Power University, Beijing, P.R.China

a253863417@qq.com, bibisi@163.com

Keywords: Capital structure; Currency credit policy; Nature of company; Financial Development

Abstract. Capital structure has been the main research topic of corporate finance and general finance.

The policy of currency influence the economy activity and balance capital, and the change of

currency credit policy will lead to direct influence financing for foreign trade from banks, thus

influence the ability of corporations to gain capital and result in the change of their capital structure.

Introduction

Features of Chinese listed company: low asset-liability ratio, high current liability. At the same time

China is in time of transition of market economy, These factors will influence the choice of Chinese

listed company. This research aims to analyze these processes and provide the theoretical evidence

for government policy and establishment of finance policy of listed company.

The research plan to demonstrate influence of credit policy on capital structure of listed

company

Model design. The model design in this research is as below:

Lev represents company's capital structure; Pol stands for currency credit policy. Size stands for

the size of company. Growth stands for company's growth. Profit stands for company's profit. Tang

stands for the proportion of company's tangible assets. Shields stands for non-debt tax shield. i stands

for the i company. t stands for the t year.

Sample selection. The time span of research is 2000-2013. Sample covers all listed company

Shanghai and Shenzhen stock trading center. Sample has been selected for optimization. Final

sample: 448 companies from general industry, 6272 samples at year 2014.

Analysis of the result and related solutions

Descriptive statistics of the sample. The number of state-owned business is over 2 times more than

non-state-owned business from 2000-2013. The average assets-liability ratio of non-state-owned

business is lower than the state-owned business, while the standard of deviation is higher, which

5th International Conference on Education, Management, Information and Medicine (EMIM 2015)

© 2015. The authors - Published by Atlantis Press 204

means the difference between this group of bigger than the one among state-owned business. Debt

payable of non-state-owned business is higher. They tend to get more non-interest debt with their

credit, while the liability with interests at non-state-owned business much higher that that of state-

owned business.

Analysis on result. This part aims to demonstrate a linear regression analysis on the relevancy

between credit policy and corporate structure. Analysis on panel data needs to check by Hausman

then choose constant benefit model and random effect model. The result of random effect model is as

Table 1, constant benefit model is as Table 2, Hausman result as Table 3.

Table 1 Random effect model

Random-effects GLS regression Number of obs = 6272

Group variable: var1 Number of groups = 448

R-sq: within = 0.3010 Obs per group: min = 14

between = 0.2710 avg = 14.0

overall = 0.2814 max = 14

Random effects u_i ~ Gaussian Wald chi2(9) = 2662.40

corr(u_i， X) = 0 (assumed) Prob > chi2 = 0.0000

lev Coef. Std. Err. z P>|z| [95% Conf. Interval]

var4 0.056964 0.023894 2.38 0.017 0.0101331 0.103796

var5 0.054877 0.023856 2.30 0.021 0.0081204 0.101633

var6 -0.01509 0.023433 -0.64 0.519 -0.0610224 0.030834

var7 0.159907 0.004146 38.57 0.000 0.1517803 0.168033

var8 -0.00052 0.000112 -4.64 0.000 -0.0007385 -0.0003

var9 0.080346 0.015832 5.08 0.000 0.0493164 0.111376

var10 -0.78411 0.037201 -21.08 0.000 -0.8570221 -0.7112

var11 0.104498 0.010724 9.74 0.000 0.0834807 0.125516

var12 -1.56543 0.126381 -12.39 0.000 -1.813133 -1.31773

_cons -1.00772 0.038427 -26.22 0.000 -1.083037 -0.9324

sigma_u .10982608

sigma_e .09571696

rho .56832119 (fraction of variance due to u_i)

205

--------------------------------------------------------------------------------------------------------------------------

var3 | Coef. Std. Err. z P>|z| [95% Conf. Interval]

------------------------------------------------------------------------------------------------------------------

var4 | .0569644 .023894 2.38 0.017 .0101331 .1037957

var5 | .0548768 .0238557 2.30 0.021 .0081204 .1016332

var6 | -.0150943 .0234331 -0.64 0.519 -.0610224 .0308339

var7 | .1599067 .0041462 38.57 0.000 .1517803 .1680331

var8 | -.0005192 .0001119 -4.64 0.000 -.0007385 -.0002999

var9 | .080346 .0158317 5.08 0.000 .0493164 .1113755

var10 | -.7841095 .037201 -21.08 0.000 -.8570221 -.7111969

var11 | .1044984 .0107235 9.74 0.000 .0834807 .1255161

var12 | -1.565431 .1263806 -12.39 0.000 -1.813133 -1.31773

_cons | -1.007721 .0384273 -26.22 0.000 -1.083037 -.9324045

---------------------------------------------------------------------------------------------------------------

sigma_u | .10982608

sigma_e | .09571696

rho | .56832119 (fraction of variance due to u_i)

-----------------------------------------------------------------------------------------------------------------------

Table 2 Constant benefit model

---------------------------------------------------------------------------------------------------------------

var3 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------------------------------------------------------------------------------------------------------------

var4 | .0481408 .0238609 2.02 0.044 .0013645 .094917

var5 | .0453031 .0238019 1.90 0.057 -.0013574 .0919637

var6 | -.0259338 .0235276 -1.10 0.270 -.0720568 .0201891

var7 | .1665546 .0044606 37.34 0.000 .1578102 .175299

var8 | -.0004485 .0001125 -3.99 0.000 -.000669 -.000228

var9 | .0740843 .0158067 4.69 0.000 .0430973 .1050713

var10 | -.7597004 .0373777 -20.32 0.000 -.8329747 -.6864261

var11 | .1047585 .0109487 9.57 0.000 .083295 .1262221

var12 | -1.450094 .1324856 -10.95 0.000 -1.709815 -1.190372

_cons | -1.068446 .0410471 -26.03 0.000 -1.148913 -.987978

---------------------------------------------------------------------------------------------------------------

sigma_u | .12146976

sigma_e | .09571696

rho | .61693042 (fraction of variance due to u_i)

---------------------------------------------------------------------------------------------------------------

F test that all u_i=0: F(447， 5815) = 20.95 Prob > F = 0.0000

Fixed-effects (within) regression Number of obs = 6272

Group variable: var1 Number of groups = 448

R-sq: within = 0.3015 Obs per group: min = 14

between = 0.2613 avg = 14.0

206

overall = 0.2749 max = 14

F(9，5815) = 278.86

corr(u_i， Xb) = -0.0858 Prob > F = 0.0000

lev Coef. Std. Err. z P>|z| [95% Conf. Interval]

var4 0.0481408 0.023861 2.02 0.044 0.001365 0.094917

var5 0.0453031 0.023802 1.90 0.057 -0.00136 0.091964

var6 -0.025934 0.023528 -1.10 0.270 -0.07206 0.020189

var7 0.1665546 0.004461 37.34 0.000 0.15781 0.175299

var8 -0.000449 0.000113 -3.99 0.000 -0.00067 -0.00023

var9 0.0740843 0.015807 4.69 0.000 0.043097 0.105071

var10 -0.7597 0.037378 -20.32 0.000 -0.83297 -0.68643

var11 0.1047585 0.010949 9.57 0.000 0.083295 0.126222

var12 -1.450094 0.132486 -10.95 0.000 -1.70982 -1.19037

_cons -1.068446 0.041047 -26.03 0.000 -1.14891 -0.98798

sigma_u 0.1214698

sigma_e 0.0571696

rho 0.6169304261693042 (fraction of variance due to u_i)

F test that all u_i=0: F(447， 5815) = 20.95 Prob > F = 0.0000

Table 3 Hausmann

（b） (B) (b-B) sqrt(diag(V_b-V_B))

fe re difference S.E.

var4 0.0481408 0.0569644 -0.0088236 .

var5 0.0453031 0.0548768 -0.0095737 .

var6 -0.0259338 -0.0150943 -0.0108396 0.0021066

var7 0.1665546 0.1599067 0.0066479 0.0016449

var8 -0.0004485 -0.0005192 0.0000707 0.0000113

var9 0.0740843 0.080346 -0.0062617 .

var10 -0.7597004 -0.7841095 0.0244091 0.0036308

var11 0.1047585 0.1044984 0.0002601 0.0022093

var12 -1.450094 -1.565431 0.1153376 0.0397543

b = consistent under Ho and Ha; obtained from xtreg

B = inconsistent under Ha， efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic

chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)

= 79.63

Prob>chi2 =

0.0000

(V_b-V_B is

not positive

definite)

It is showed as above that P: 0<0.05, while chi 79.63 is positive, therefore the panel data

regression denies random effect. The constant benefit model should be used for it.

Further analysis is as Table 2. Variables sum as 6272. Group variable is time. There are 448

groups, i.e. we could see stata regression of constant benefit in 448 companies between 2000-2013.

Goodness-of-fit of this model: 0.3015 within group and 0.613 among groups, averaging 0.2749.

207

Column coef stands for variables and relativeness between variables. There is negative correlation

between variable 6, 8, 10 and 12, the rest are with positive correlation. The P of variable 6 equals

0.270, which exceeds 10%. Therefore variable 6 (credit policy in year t-2) should be excluded, which

means the relativeness of capital structure between year t and t-2 is weak. Variable 4 and 5 is within

5%, and the P of variable 7-12 equals 0.000, which means the relativeness between them is strong.

Result: the relativeness of capital structure of listed company between year t-1 and t-2 is weak. This

is because the incremental rate of loan balance of inland finance department is used as index for

currency policy.

Conclusion and Proposal

The influence of currency credit policy is different on debt ratio of different kinds of business, on

capital structure of different areas with finance development difference, and on company with

different operation results. Therefore, we need to accelerate the market reform of company bonds,

we need to balance the finance development among areas.

References

[1] D Durand. Costs of Debt and Equity Funds for Business: Trends and Problems of Measurement

［Z］.Conference on Research in Business Finance, 1952

[2] DeAngelo, H. and R. Masulis, Optimal capital structure under corporate and Personal taxation [J],

Journal of Financial Economics.1980, 3-29

[3] Dincergok, B. & Yalciner, K. (2011) Capital structure decisions of manufacturing firms’ in

developing countries, Middle Eastern finance and economics, 12, April, 2011, 86–100

[4] Keshtkar, R. Valipour, H. & Javanmard, A. (2012) Determinants of corporate capital structure

under different debt maturities: empirical evidence from Iran, International research journal of

finance and economics, 90, May, 46–53

[5] Bhaumika, Dangb,Kutan Implications of bank ownership for the credit channel of monetary

policy transmission: Evidence from India

[6] Lim M, T. C. (2012). Determinants of capital structure: empirical evidence from financial

services listed firms in China, International journal of economics and finance, 4(3), 191–203

[7] Kouki, M. & Said, H. B. (2012). Capital structure determinants: new evidence from French panel

data, International journal of business and management, 7(1), 214–229

[8] Matteo Iacoviello, Raoul Minetti. The Credit Channel of Monetary Policy: Evidence from the

Housing Market [J]. Journal of Macroeconomics, 2008, Vol 30, N0.1:69-96

[9] Iris Claus .Inside the black box: How important is the credit channel relative to the interest and

exchange rate channels? [J] Economic Modeling, 2011, Volume 28, Issues 1–2, Pages 1–12

[10] Ries Wulandari Do Credit Channel and Interest Rate Channel Play Important Role in Monetary

Transmission Mechanism in Indonesia?: A Structural Vector Autoregression Model

208